Overview

Dataset statistics

Number of variables26
Number of observations7000
Missing cells65
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory216.0 B

Variable types

Categorical11
Numeric14
Unsupported1

Alerts

Name has a high cardinality: 6241 distinct valuesHigh cardinality
Income is highly overall correlated with MntMeat&Fish and 9 other fieldsHigh correlation
MntMeat&Fish is highly overall correlated with Income and 8 other fieldsHigh correlation
MntEntries is highly overall correlated with Income and 7 other fieldsHigh correlation
MntVegan&Vegetarian is highly overall correlated with Income and 9 other fieldsHigh correlation
MntDrinks is highly overall correlated with Income and 7 other fieldsHigh correlation
MntDesserts is highly overall correlated with Income and 7 other fieldsHigh correlation
MntAdditionalRequests is highly overall correlated with MntMeat&Fish and 7 other fieldsHigh correlation
NumAppPurchases is highly overall correlated with Income and 5 other fieldsHigh correlation
NumTakeAwayPurchases is highly overall correlated with Income and 9 other fieldsHigh correlation
NumStorePurchases is highly overall correlated with Income and 8 other fieldsHigh correlation
NumAppVisitsMonth is highly overall correlated with Income and 2 other fieldsHigh correlation
Response_Cmp3 is highly overall correlated with IncomeHigh correlation
Response_Cmp1 is highly imbalanced (60.1%)Imbalance
Response_Cmp2 is highly imbalanced (62.2%)Imbalance
Response_Cmp3 is highly imbalanced (58.7%)Imbalance
Response_Cmp4 is highly imbalanced (65.0%)Imbalance
Response_Cmp5 is highly imbalanced (89.2%)Imbalance
Complain is highly imbalanced (91.7%)Imbalance
Name is uniformly distributedUniform
Date_Adherence is an unsupported type, check if it needs cleaning or further analysisUnsupported
MntEntries has 1160 (16.6%) zerosZeros
MntDrinks has 1177 (16.8%) zerosZeros
MntDesserts has 1162 (16.6%) zerosZeros
MntAdditionalRequests has 164 (2.3%) zerosZeros
NumOfferPurchases has 184 (2.6%) zerosZeros
NumAppPurchases has 84 (1.2%) zerosZeros
NumTakeAwayPurchases has 83 (1.2%) zerosZeros
NumStorePurchases has 98 (1.4%) zerosZeros
NumAppVisitsMonth has 78 (1.1%) zerosZeros

Reproduction

Analysis started2023-03-26 15:55:41.654741
Analysis finished2023-03-26 15:56:19.183915
Duration37.53 seconds
Software versionydata-profiling vv4.1.1
Download configurationconfig.json

Variables

Name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct6241
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size109.4 KiB
Mr. Christopher Hamilton
 
3
Mr. Alexander McDonald
 
3
Mr. Justin Quinn
 
3
Mr. Christian Hardacre
 
3
Mr. Trevor Paterson
 
3
Other values (6236)
6985 

Length

Max length25
Median length22
Mean length16.933286
Min length11

Characters and Unicode

Total characters118533
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5525 ?
Unique (%)78.9%

Sample

1st rowMr. Isaac Arnold
2nd rowMr. Austin Abraham
3rd rowMrs. Bernadette Allan
4th rowMrs. Kylie Russell
5th rowMr. Julian Arnold

Common Values

ValueCountFrequency (%)
Mr. Christopher Hamilton 3
 
< 0.1%
Mr. Alexander McDonald 3
 
< 0.1%
Mr. Justin Quinn 3
 
< 0.1%
Mr. Christian Hardacre 3
 
< 0.1%
Mr. Trevor Paterson 3
 
< 0.1%
Mr. Trevor Morgan 3
 
< 0.1%
Mr. Connor Simpson 3
 
< 0.1%
Mr. Stewart Grant 3
 
< 0.1%
Mr. Simon Abraham 3
 
< 0.1%
Mr. Stewart Lewis 3
 
< 0.1%
Other values (6231) 6970
99.6%

Length

2023-03-26T16:56:19.316531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mr 5133
 
24.4%
miss 1469
 
7.0%
mrs 398
 
1.9%
stewart 139
 
0.7%
james 121
 
0.6%
oliver 110
 
0.5%
blake 108
 
0.5%
cameron 91
 
0.4%
dylan 79
 
0.4%
phil 79
 
0.4%
Other values (301) 13273
63.2%

Most occurring characters

ValueCountFrequency (%)
14000
 
11.8%
r 11837
 
10.0%
a 8864
 
7.5%
M 8252
 
7.0%
e 7484
 
6.3%
n 7338
 
6.2%
s 6940
 
5.9%
i 6411
 
5.4%
. 5531
 
4.7%
l 4864
 
4.1%
Other values (42) 37012
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 77762
65.6%
Uppercase Letter 21240
 
17.9%
Space Separator 14000
 
11.8%
Other Punctuation 5531
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 11837
15.2%
a 8864
11.4%
e 7484
9.6%
n 7338
9.4%
s 6940
8.9%
i 6411
8.2%
l 4864
 
6.3%
o 4712
 
6.1%
t 2990
 
3.8%
h 2714
 
3.5%
Other values (15) 13608
17.5%
Uppercase Letter
ValueCountFrequency (%)
M 8252
38.9%
S 1188
 
5.6%
J 1169
 
5.5%
A 990
 
4.7%
B 967
 
4.6%
C 934
 
4.4%
P 871
 
4.1%
D 746
 
3.5%
L 741
 
3.5%
H 697
 
3.3%
Other values (15) 4685
22.1%
Space Separator
ValueCountFrequency (%)
14000
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5531
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 99002
83.5%
Common 19531
 
16.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 11837
12.0%
a 8864
 
9.0%
M 8252
 
8.3%
e 7484
 
7.6%
n 7338
 
7.4%
s 6940
 
7.0%
i 6411
 
6.5%
l 4864
 
4.9%
o 4712
 
4.8%
t 2990
 
3.0%
Other values (40) 29310
29.6%
Common
ValueCountFrequency (%)
14000
71.7%
. 5531
 
28.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118533
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14000
 
11.8%
r 11837
 
10.0%
a 8864
 
7.5%
M 8252
 
7.0%
e 7484
 
6.3%
n 7338
 
6.2%
s 6940
 
5.9%
i 6411
 
5.4%
. 5531
 
4.7%
l 4864
 
4.1%
Other values (42) 37012
31.2%

Birthyear
Real number (ℝ)

Distinct58
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1976.4514
Minimum1948
Maximum2005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:19.505192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1948
5-th percentile1957
Q11967
median1977
Q31985
95-th percentile1995
Maximum2005
Range57
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.99627
Coefficient of variation (CV)0.0060696
Kurtosis-0.85734076
Mean1976.4514
Median Absolute Deviation (MAD)9
Skewness-0.092477866
Sum13835160
Variance143.91049
MonotonicityNot monotonic
2023-03-26T16:56:19.662826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1985 242
 
3.5%
1981 238
 
3.4%
1983 231
 
3.3%
1982 226
 
3.2%
1979 220
 
3.1%
1984 217
 
3.1%
1977 214
 
3.1%
1980 207
 
3.0%
1986 198
 
2.8%
1976 197
 
2.8%
Other values (48) 4810
68.7%
ValueCountFrequency (%)
1948 4
 
0.1%
1949 6
 
0.1%
1950 21
 
0.3%
1951 19
 
0.3%
1952 26
 
0.4%
1953 34
 
0.5%
1954 59
0.8%
1955 59
0.8%
1956 91
1.3%
1957 100
1.4%
ValueCountFrequency (%)
2005 4
 
0.1%
2004 1
 
< 0.1%
2003 6
 
0.1%
2002 17
 
0.2%
2001 22
 
0.3%
2000 29
 
0.4%
1999 35
0.5%
1998 67
1.0%
1997 84
1.2%
1996 81
1.2%

Education
Categorical

Distinct9
Distinct (%)0.1%
Missing14
Missing (%)0.2%
Memory size109.4 KiB
Graduation
3497 
PhD
1494 
Master
1135 
HighSchool
663 
Basic
 
179
Other values (4)
 
18

Length

Max length10
Median length10
Mean length7.7190094
Min length3

Characters and Unicode

Total characters53925
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowPhD
3rd rowMaster
4th rowPhD
5th rowMaster

Common Values

ValueCountFrequency (%)
Graduation 3497
50.0%
PhD 1494
21.3%
Master 1135
 
16.2%
HighSchool 663
 
9.5%
Basic 179
 
2.6%
graduation 7
 
0.1%
master 7
 
0.1%
highschool 2
 
< 0.1%
phd 2
 
< 0.1%
(Missing) 14
 
0.2%

Length

2023-03-26T16:56:19.826466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-26T16:56:20.027138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
graduation 3504
50.2%
phd 1496
21.4%
master 1142
 
16.3%
highschool 665
 
9.5%
basic 179
 
2.6%

Most occurring characters

ValueCountFrequency (%)
a 8329
15.4%
o 4834
9.0%
t 4646
8.6%
r 4646
8.6%
i 4348
8.1%
d 3506
 
6.5%
u 3504
 
6.5%
n 3504
 
6.5%
G 3497
 
6.5%
h 2828
 
5.2%
Other values (13) 10283
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44800
83.1%
Uppercase Letter 9125
 
16.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8329
18.6%
o 4834
10.8%
t 4646
10.4%
r 4646
10.4%
i 4348
9.7%
d 3506
7.8%
u 3504
7.8%
n 3504
7.8%
h 2828
 
6.3%
s 1323
 
3.0%
Other values (6) 3332
7.4%
Uppercase Letter
ValueCountFrequency (%)
G 3497
38.3%
D 1494
16.4%
P 1494
16.4%
M 1135
 
12.4%
H 663
 
7.3%
S 663
 
7.3%
B 179
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53925
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8329
15.4%
o 4834
9.0%
t 4646
8.6%
r 4646
8.6%
i 4348
8.1%
d 3506
 
6.5%
u 3504
 
6.5%
n 3504
 
6.5%
G 3497
 
6.5%
h 2828
 
5.2%
Other values (13) 10283
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8329
15.4%
o 4834
9.0%
t 4646
8.6%
r 4646
8.6%
i 4348
8.1%
d 3506
 
6.5%
u 3504
 
6.5%
n 3504
 
6.5%
G 3497
 
6.5%
h 2828
 
5.2%
Other values (13) 10283
19.1%

Marital_Status
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size109.4 KiB
Married
2830 
Together
1683 
Single
1525 
Divorced
637 
Widow
 
243
Other values (5)
 
82

Length

Max length8
Median length7
Mean length7.0461429
Min length5

Characters and Unicode

Total characters49323
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTogether
2nd rowDivorced
3rd rowDivorced
4th rowDivorced
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 2830
40.4%
Together 1683
24.0%
Single 1525
21.8%
Divorced 637
 
9.1%
Widow 243
 
3.5%
married 36
 
0.5%
together 23
 
0.3%
single 13
 
0.2%
divorced 8
 
0.1%
widow 2
 
< 0.1%

Length

2023-03-26T16:56:20.210295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-26T16:56:20.395955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
married 2866
40.9%
together 1706
24.4%
single 1538
22.0%
divorced 645
 
9.2%
widow 245
 
3.5%

Most occurring characters

ValueCountFrequency (%)
e 8461
17.2%
r 8083
16.4%
i 5294
10.7%
d 3764
7.6%
g 3244
 
6.6%
a 2866
 
5.8%
M 2830
 
5.7%
o 2596
 
5.3%
t 1729
 
3.5%
h 1706
 
3.5%
Other values (11) 8750
17.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42405
86.0%
Uppercase Letter 6918
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8461
20.0%
r 8083
19.1%
i 5294
12.5%
d 3764
8.9%
g 3244
 
7.7%
a 2866
 
6.8%
o 2596
 
6.1%
t 1729
 
4.1%
h 1706
 
4.0%
n 1538
 
3.6%
Other values (6) 3124
 
7.4%
Uppercase Letter
ValueCountFrequency (%)
M 2830
40.9%
T 1683
24.3%
S 1525
22.0%
D 637
 
9.2%
W 243
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 49323
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8461
17.2%
r 8083
16.4%
i 5294
10.7%
d 3764
7.6%
g 3244
 
6.6%
a 2866
 
5.8%
M 2830
 
5.7%
o 2596
 
5.3%
t 1729
 
3.5%
h 1706
 
3.5%
Other values (11) 8750
17.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8461
17.2%
r 8083
16.4%
i 5294
10.7%
d 3764
7.6%
g 3244
 
6.6%
a 2866
 
5.8%
M 2830
 
5.7%
o 2596
 
5.3%
t 1729
 
3.5%
h 1706
 
3.5%
Other values (11) 8750
17.7%

Income
Real number (ℝ)

Distinct6732
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77988.962
Minimum2493.8
Maximum237639.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:20.582615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2493.8
5-th percentile26521.9
Q151586.25
median77190
Q3102016.25
95-th percentile126717
Maximum237639.73
Range235145.93
Interquartile range (IQR)50430

Descriptive statistics

Standard deviation35409.81
Coefficient of variation (CV)0.45403618
Kurtosis2.6490352
Mean77988.962
Median Absolute Deviation (MAD)25284.5
Skewness0.8447209
Sum5.4592274 × 108
Variance1.2538547 × 109
MonotonicityNot monotonic
2023-03-26T16:56:20.754762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11000 28
 
0.4%
104299 4
 
0.1%
72803 3
 
< 0.1%
91801 3
 
< 0.1%
42343 3
 
< 0.1%
73086 3
 
< 0.1%
91594 3
 
< 0.1%
56918 3
 
< 0.1%
54943 3
 
< 0.1%
77352 3
 
< 0.1%
Other values (6722) 6944
99.2%
ValueCountFrequency (%)
2493.8 1
< 0.1%
3005.4 1
< 0.1%
3133.7 1
< 0.1%
3265.8 1
< 0.1%
3464.1 1
< 0.1%
3478 1
< 0.1%
3550 1
< 0.1%
3623.7 1
< 0.1%
3746.5 1
< 0.1%
3827.3 1
< 0.1%
ValueCountFrequency (%)
237639.725 1
< 0.1%
237322.825 1
< 0.1%
236745.625 1
< 0.1%
236316.125 1
< 0.1%
236287.625 1
< 0.1%
236217.225 1
< 0.1%
236195.425 1
< 0.1%
236158.525 1
< 0.1%
235713.425 1
< 0.1%
235452.425 1
< 0.1%

Kid_Younger6
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.4 KiB
0
4087 
1
2742 
2
 
171

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4087
58.4%
1 2742
39.2%
2 171
 
2.4%

Length

2023-03-26T16:56:20.894882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-26T16:56:21.037504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4087
58.4%
1 2742
39.2%
2 171
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 4087
58.4%
1 2742
39.2%
2 171
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4087
58.4%
1 2742
39.2%
2 171
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 7000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4087
58.4%
1 2742
39.2%
2 171
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4087
58.4%
1 2742
39.2%
2 171
 
2.4%

Children_6to18
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.4 KiB
0
3720 
1
3126 
2
 
154

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 3720
53.1%
1 3126
44.7%
2 154
 
2.2%

Length

2023-03-26T16:56:21.163613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-26T16:56:21.317245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3720
53.1%
1 3126
44.7%
2 154
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 3720
53.1%
1 3126
44.7%
2 154
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3720
53.1%
1 3126
44.7%
2 154
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 7000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3720
53.1%
1 3126
44.7%
2 154
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3720
53.1%
1 3126
44.7%
2 154
 
2.2%

Response_Cmp1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.4 KiB
0
6446 
1
 
554

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6446
92.1%
1 554
 
7.9%

Length

2023-03-26T16:56:21.435346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-26T16:56:21.576467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 6446
92.1%
1 554
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0 6446
92.1%
1 554
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6446
92.1%
1 554
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 7000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6446
92.1%
1 554
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6446
92.1%
1 554
 
7.9%

Response_Cmp2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.4 KiB
0
6487 
1
 
513

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6487
92.7%
1 513
 
7.3%

Length

2023-03-26T16:56:21.685060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-26T16:56:21.831686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 6487
92.7%
1 513
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 6487
92.7%
1 513
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6487
92.7%
1 513
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 7000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6487
92.7%
1 513
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6487
92.7%
1 513
 
7.3%

Response_Cmp3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.4 KiB
0
6419 
1
 
581

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6419
91.7%
1 581
 
8.3%

Length

2023-03-26T16:56:21.946784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-26T16:56:22.089407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 6419
91.7%
1 581
 
8.3%

Most occurring characters

ValueCountFrequency (%)
0 6419
91.7%
1 581
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6419
91.7%
1 581
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 7000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6419
91.7%
1 581
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6419
91.7%
1 581
 
8.3%

Response_Cmp4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.4 KiB
0
6539 
1
 
461

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 6539
93.4%
1 461
 
6.6%

Length

2023-03-26T16:56:22.206506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-26T16:56:22.345670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 6539
93.4%
1 461
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 6539
93.4%
1 461
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6539
93.4%
1 461
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 7000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6539
93.4%
1 461
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6539
93.4%
1 461
 
6.6%

Response_Cmp5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.4 KiB
0
6900 
1
 
100

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6900
98.6%
1 100
 
1.4%

Length

2023-03-26T16:56:22.455721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-26T16:56:22.590836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 6900
98.6%
1 100
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 6900
98.6%
1 100
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6900
98.6%
1 100
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 7000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6900
98.6%
1 100
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6900
98.6%
1 100
 
1.4%

Date_Adherence
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size109.4 KiB

Recency
Real number (ℝ)

Distinct100
Distinct (%)1.4%
Missing23
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean49.235058
Minimum0
Maximum99
Zeros65
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:22.741465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile95
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.922688
Coefficient of variation (CV)0.58744092
Kurtosis-1.1979782
Mean49.235058
Median Absolute Deviation (MAD)25
Skewness0.017675703
Sum343513
Variance836.52186
MonotonicityNot monotonic
2023-03-26T16:56:22.917617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 86
 
1.2%
24 85
 
1.2%
64 84
 
1.2%
30 83
 
1.2%
4 82
 
1.2%
92 82
 
1.2%
46 82
 
1.2%
9 82
 
1.2%
45 81
 
1.2%
26 80
 
1.1%
Other values (90) 6150
87.9%
ValueCountFrequency (%)
0 65
0.9%
1 78
1.1%
2 66
0.9%
3 78
1.1%
4 82
1.2%
5 59
0.8%
6 69
1.0%
7 70
1.0%
8 70
1.0%
9 82
1.2%
ValueCountFrequency (%)
99 59
0.8%
98 76
1.1%
97 76
1.1%
96 76
1.1%
95 74
1.1%
94 71
1.0%
93 68
1.0%
92 82
1.2%
91 63
0.9%
90 76
1.1%

MntMeat&Fish
Real number (ℝ)

Distinct1343
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3079.5238
Minimum0
Maximum14980
Zeros42
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:23.099773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1250
median1820
Q35070
95-th percentile10020
Maximum14980
Range14980
Interquartile range (IQR)4820

Descriptive statistics

Standard deviation3370.3772
Coefficient of variation (CV)1.0944475
Kurtosis0.5320977
Mean3079.5238
Median Absolute Deviation (MAD)1718
Skewness1.1488619
Sum21556667
Variance11359442
MonotonicityNot monotonic
2023-03-26T16:56:23.264914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 121
 
1.7%
40 116
 
1.7%
30 101
 
1.4%
10 100
 
1.4%
60 98
 
1.4%
50 92
 
1.3%
120 83
 
1.2%
80 76
 
1.1%
70 73
 
1.0%
90 73
 
1.0%
Other values (1333) 6067
86.7%
ValueCountFrequency (%)
0 42
0.6%
2 1
 
< 0.1%
3 2
 
< 0.1%
4 4
 
0.1%
5 4
 
0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
9 4
 
0.1%
10 100
1.4%
ValueCountFrequency (%)
14980 1
< 0.1%
14970 1
< 0.1%
14940 1
< 0.1%
14930 1
< 0.1%
14920 1
< 0.1%
14890 1
< 0.1%
14860 1
< 0.1%
14810 1
< 0.1%
14780 1
< 0.1%
14720 1
< 0.1%

MntEntries
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct378
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean534.74943
Minimum0
Maximum3980
Zeros1160
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:23.448072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140
median180
Q3680
95-th percentile2380
Maximum3980
Range3980
Interquartile range (IQR)640

Descriptive statistics

Standard deviation787.84668
Coefficient of variation (CV)1.4733007
Kurtosis4.096167
Mean534.74943
Median Absolute Deviation (MAD)180
Skewness2.08722
Sum3743246
Variance620702.4
MonotonicityNot monotonic
2023-03-26T16:56:23.620719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1160
 
16.6%
20 469
 
6.7%
40 375
 
5.4%
60 336
 
4.8%
80 266
 
3.8%
100 215
 
3.1%
140 208
 
3.0%
120 180
 
2.6%
160 139
 
2.0%
180 129
 
1.8%
Other values (368) 3523
50.3%
ValueCountFrequency (%)
0 1160
16.6%
2 8
 
0.1%
4 9
 
0.1%
6 8
 
0.1%
8 10
 
0.1%
10 9
 
0.1%
12 3
 
< 0.1%
14 4
 
0.1%
16 7
 
0.1%
18 2
 
< 0.1%
ValueCountFrequency (%)
3980 2
 
< 0.1%
3960 1
 
< 0.1%
3940 7
0.1%
3900 1
 
< 0.1%
3880 4
0.1%
3860 2
 
< 0.1%
3840 4
0.1%
3820 2
 
< 0.1%
3800 3
< 0.1%
3780 5
0.1%

MntVegan&Vegetarian
Real number (ℝ)

Distinct1039
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2785.0508
Minimum0
Maximum25974
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:23.799873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q1240
median1110
Q33795
95-th percentile10755
Maximum25974
Range25974
Interquartile range (IQR)3555

Descriptive statistics

Standard deviation3908.7182
Coefficient of variation (CV)1.4034639
Kurtosis8.4315383
Mean2785.0508
Median Absolute Deviation (MAD)975
Skewness2.4868973
Sum19495356
Variance15278078
MonotonicityNot monotonic
2023-03-26T16:56:23.970519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 145
 
2.1%
150 136
 
1.9%
75 134
 
1.9%
90 133
 
1.9%
180 126
 
1.8%
165 122
 
1.7%
60 117
 
1.7%
120 115
 
1.6%
135 105
 
1.5%
45 103
 
1.5%
Other values (1029) 5764
82.3%
ValueCountFrequency (%)
0 3
 
< 0.1%
1.5 1
 
< 0.1%
4.5 4
 
0.1%
7.5 2
 
< 0.1%
10.5 2
 
< 0.1%
12 1
 
< 0.1%
13.5 1
 
< 0.1%
15 38
0.5%
16.5 4
 
0.1%
18 1
 
< 0.1%
ValueCountFrequency (%)
25974 1
< 0.1%
25819.5 1
< 0.1%
25768.5 1
< 0.1%
25713 1
< 0.1%
25614 1
< 0.1%
25474.5 1
< 0.1%
25351.5 1
< 0.1%
25290 1
< 0.1%
25284 1
< 0.1%
25237.5 1
< 0.1%

MntDrinks
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct370
Distinct (%)5.3%
Missing28
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean545.65754
Minimum0
Maximum3980
Zeros1177
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:24.152176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140
median180
Q3700
95-th percentile2449
Maximum3980
Range3980
Interquartile range (IQR)660

Descriptive statistics

Standard deviation805.14909
Coefficient of variation (CV)1.4755575
Kurtosis3.839256
Mean545.65754
Median Absolute Deviation (MAD)180
Skewness2.0463077
Sum3804324.4
Variance648265.05
MonotonicityNot monotonic
2023-03-26T16:56:24.333330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1177
 
16.8%
20 449
 
6.4%
40 367
 
5.2%
60 345
 
4.9%
80 244
 
3.5%
100 213
 
3.0%
140 202
 
2.9%
120 176
 
2.5%
160 165
 
2.4%
200 146
 
2.1%
Other values (360) 3488
49.8%
ValueCountFrequency (%)
0 1177
16.8%
2 12
 
0.2%
4 7
 
0.1%
6 10
 
0.1%
8 4
 
0.1%
10 6
 
0.1%
12 5
 
0.1%
14 4
 
0.1%
16 4
 
0.1%
18 2
 
< 0.1%
ValueCountFrequency (%)
3980 3
< 0.1%
3960 4
0.1%
3940 5
0.1%
3920 2
 
< 0.1%
3900 3
< 0.1%
3880 4
0.1%
3840 5
0.1%
3820 2
 
< 0.1%
3800 3
< 0.1%
3780 5
0.1%

MntDesserts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct364
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540.65603
Minimum0
Maximum3980
Zeros1162
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:24.520491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140
median180
Q3680
95-th percentile2500
Maximum3980
Range3980
Interquartile range (IQR)640

Descriptive statistics

Standard deviation802.22187
Coefficient of variation (CV)1.4837934
Kurtosis3.8134636
Mean540.65603
Median Absolute Deviation (MAD)180
Skewness2.0578112
Sum3784592.2
Variance643559.92
MonotonicityNot monotonic
2023-03-26T16:56:24.695140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1162
 
16.6%
20 456
 
6.5%
40 396
 
5.7%
60 345
 
4.9%
80 234
 
3.3%
100 221
 
3.2%
140 183
 
2.6%
120 168
 
2.4%
160 153
 
2.2%
280 123
 
1.8%
Other values (354) 3559
50.8%
ValueCountFrequency (%)
0 1162
16.6%
2 18
 
0.3%
4 3
 
< 0.1%
6 8
 
0.1%
8 5
 
0.1%
10 4
 
0.1%
12 6
 
0.1%
14 5
 
0.1%
16 6
 
0.1%
18 2
 
< 0.1%
ValueCountFrequency (%)
3980 3
< 0.1%
3960 2
 
< 0.1%
3940 5
0.1%
3920 2
 
< 0.1%
3900 1
 
< 0.1%
3880 2
 
< 0.1%
3860 1
 
< 0.1%
3840 4
0.1%
3820 4
0.1%
3780 5
0.1%

MntAdditionalRequests
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct324
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.556186
Minimum0
Maximum249
Zeros164
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:25.253619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24
Q357
95-th percentile155
Maximum249
Range249
Interquartile range (IQR)48

Descriptive statistics

Standard deviation49.650747
Coefficient of variation (CV)1.1667105
Kurtosis3.0841903
Mean42.556186
Median Absolute Deviation (MAD)19
Skewness1.8265224
Sum297893.3
Variance2465.1967
MonotonicityNot monotonic
2023-03-26T16:56:25.428770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 214
 
3.1%
2 202
 
2.9%
4 198
 
2.8%
1 197
 
2.8%
7 179
 
2.6%
5 169
 
2.4%
0 164
 
2.3%
9 158
 
2.3%
6 156
 
2.2%
12 155
 
2.2%
Other values (314) 5208
74.4%
ValueCountFrequency (%)
0 164
2.3%
0.1 6
 
0.1%
0.2 3
 
< 0.1%
0.3 7
 
0.1%
0.4 5
 
0.1%
0.5 2
 
< 0.1%
0.6 3
 
< 0.1%
0.7 2
 
< 0.1%
0.8 4
 
0.1%
0.9 1
 
< 0.1%
ValueCountFrequency (%)
249 5
0.1%
247 1
 
< 0.1%
246 6
0.1%
245 3
< 0.1%
244 2
 
< 0.1%
243 1
 
< 0.1%
242 1
 
< 0.1%
241 5
0.1%
240 2
 
< 0.1%
239 1
 
< 0.1%

NumOfferPurchases
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4484286
Minimum0
Maximum16
Zeros184
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:25.569390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3069679
Coefficient of variation (CV)0.94222387
Kurtosis10.997946
Mean2.4484286
Median Absolute Deviation (MAD)1
Skewness2.8605144
Sum17139
Variance5.3221007
MonotonicityNot monotonic
2023-03-26T16:56:25.698001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 2982
42.6%
2 1510
21.6%
3 929
 
13.3%
4 581
 
8.3%
5 271
 
3.9%
0 184
 
2.6%
6 178
 
2.5%
7 129
 
1.8%
15 83
 
1.2%
8 54
 
0.8%
Other values (7) 99
 
1.4%
ValueCountFrequency (%)
0 184
 
2.6%
1 2982
42.6%
2 1510
21.6%
3 929
 
13.3%
4 581
 
8.3%
5 271
 
3.9%
6 178
 
2.5%
7 129
 
1.8%
8 54
 
0.8%
9 45
 
0.6%
ValueCountFrequency (%)
16 5
 
0.1%
15 83
1.2%
14 2
 
< 0.1%
13 4
 
0.1%
12 9
 
0.1%
11 10
 
0.1%
10 24
 
0.3%
9 45
 
0.6%
8 54
0.8%
7 129
1.8%

NumAppPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0157143
Minimum0
Maximum13
Zeros84
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:25.829112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median6
Q38
95-th percentile11
Maximum13
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7455369
Coefficient of variation (CV)0.45639416
Kurtosis-0.22289894
Mean6.0157143
Median Absolute Deviation (MAD)2
Skewness0.52587281
Sum42110
Variance7.5379728
MonotonicityNot monotonic
2023-03-26T16:56:25.958724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
4 1143
16.3%
3 1043
14.9%
5 1015
14.5%
6 867
12.4%
7 741
10.6%
8 598
8.5%
9 467
6.7%
10 328
 
4.7%
11 237
 
3.4%
12 181
 
2.6%
Other values (4) 380
 
5.4%
ValueCountFrequency (%)
0 84
 
1.2%
1 90
 
1.3%
2 79
 
1.1%
3 1043
14.9%
4 1143
16.3%
5 1015
14.5%
6 867
12.4%
7 741
10.6%
8 598
8.5%
9 467
6.7%
ValueCountFrequency (%)
13 127
 
1.8%
12 181
 
2.6%
11 237
 
3.4%
10 328
 
4.7%
9 467
6.7%
8 598
8.5%
7 741
10.6%
6 867
12.4%
5 1015
14.5%
4 1143
16.3%

NumTakeAwayPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8345714
Minimum0
Maximum24
Zeros83
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:26.090338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q35
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3311416
Coefficient of variation (CV)0.86871288
Kurtosis8.5821378
Mean3.8345714
Median Absolute Deviation (MAD)2
Skewness2.2541583
Sum26842
Variance11.096504
MonotonicityNot monotonic
2023-03-26T16:56:26.230957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 1712
24.5%
2 1462
20.9%
3 945
13.5%
4 576
 
8.2%
5 557
 
8.0%
6 400
 
5.7%
7 382
 
5.5%
8 266
 
3.8%
9 200
 
2.9%
10 142
 
2.0%
Other values (5) 358
 
5.1%
ValueCountFrequency (%)
0 83
 
1.2%
1 1712
24.5%
2 1462
20.9%
3 945
13.5%
4 576
 
8.2%
5 557
 
8.0%
6 400
 
5.7%
7 382
 
5.5%
8 266
 
3.8%
9 200
 
2.9%
ValueCountFrequency (%)
24 13
 
0.2%
23 48
 
0.7%
12 90
 
1.3%
11 124
 
1.8%
10 142
 
2.0%
9 200
 
2.9%
8 266
3.8%
7 382
5.5%
6 400
5.7%
5 557
8.0%

NumStorePurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7905714
Minimum0
Maximum13
Zeros98
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:26.352061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.295708
Coefficient of variation (CV)0.56915074
Kurtosis-0.69432689
Mean5.7905714
Median Absolute Deviation (MAD)2
Skewness0.62325366
Sum40534
Variance10.861691
MonotonicityNot monotonic
2023-03-26T16:56:26.488678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 1506
21.5%
4 1058
15.1%
2 620
8.9%
6 546
 
7.8%
5 543
 
7.8%
8 495
 
7.1%
7 458
 
6.5%
10 377
 
5.4%
9 359
 
5.1%
12 322
 
4.6%
Other values (4) 716
10.2%
ValueCountFrequency (%)
0 98
 
1.4%
1 76
 
1.1%
2 620
8.9%
3 1506
21.5%
4 1058
15.1%
5 543
 
7.8%
6 546
 
7.8%
7 458
 
6.5%
8 495
 
7.1%
9 359
 
5.1%
ValueCountFrequency (%)
13 242
 
3.5%
12 322
 
4.6%
11 300
 
4.3%
10 377
 
5.4%
9 359
 
5.1%
8 495
7.1%
7 458
6.5%
6 546
7.8%
5 543
7.8%
4 1058
15.1%

NumAppVisitsMonth
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2782857
Minimum0
Maximum20
Zeros78
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size109.4 KiB
2023-03-26T16:56:26.626796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7485959
Coefficient of variation (CV)0.52073647
Kurtosis4.9928703
Mean5.2782857
Median Absolute Deviation (MAD)2
Skewness1.005192
Sum36948
Variance7.5547792
MonotonicityNot monotonic
2023-03-26T16:56:26.751403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
7 1116
15.9%
6 996
14.2%
8 965
13.8%
5 897
12.8%
4 684
9.8%
3 648
9.3%
2 641
9.2%
1 558
8.0%
9 350
 
5.0%
0 78
 
1.1%
Other values (3) 67
 
1.0%
ValueCountFrequency (%)
0 78
 
1.1%
1 558
8.0%
2 641
9.2%
3 648
9.3%
4 684
9.8%
5 897
12.8%
6 996
14.2%
7 1116
15.9%
8 965
13.8%
9 350
 
5.0%
ValueCountFrequency (%)
20 39
 
0.6%
19 25
 
0.4%
10 3
 
< 0.1%
9 350
 
5.0%
8 965
13.8%
7 1116
15.9%
6 996
14.2%
5 897
12.8%
4 684
9.8%
3 648
9.3%

Complain
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.4 KiB
0
6928 
1
 
72

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6928
99.0%
1 72
 
1.0%

Length

2023-03-26T16:56:26.903033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-26T16:56:27.033645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 6928
99.0%
1 72
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 6928
99.0%
1 72
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6928
99.0%
1 72
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6928
99.0%
1 72
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6928
99.0%
1 72
 
1.0%

Interactions

2023-03-26T16:56:15.489749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:45.425975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:48.000181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:50.401739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:52.530565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:54.795006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:57.231595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:59.728736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:02.108776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:04.666470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:06.783785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:09.000685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:11.162039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:13.270846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:15.663897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:45.617637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:48.151810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:50.539858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:52.681195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:54.975660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:57.399740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:59.890375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:02.263409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:04.813596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:06.931412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:09.149812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:11.311166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:13.419474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:15.812026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:45.787785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:48.289429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:50.691488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:52.830323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:55.133797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:57.556373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:00.059019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:02.413537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:04.941205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:07.082040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:09.295438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:11.445781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:13.552088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:16.317958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:45.958930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:48.431050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:50.824103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:52.987959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:55.289429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:57.711006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:00.232167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:02.568671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:05.101342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:07.227165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:09.437560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:11.581398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:13.700214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:16.474593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:46.134080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:48.850409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:50.982737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:53.160104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:55.467583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:57.888159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:00.411321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:03.100627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:05.258978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:07.386301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:09.593193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:11.740034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:13.851344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:16.633729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:46.325244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:49.013048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:51.143376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:53.332252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:55.646236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:58.077821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:00.630008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:03.267770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:05.418614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:07.546939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:09.751329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:11.900171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:14.012482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:16.778853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:46.540429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:49.182194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:51.293504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:53.484382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:55.808876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:58.248467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:00.810164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:03.415397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:05.567242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:07.699069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:09.908465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:12.052802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:14.164112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:16.946998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:46.734596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:49.341330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:51.454141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:53.648522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:55.987529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:58.421615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:00.987315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:03.574533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:05.716370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:07.866212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:10.072604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:12.214942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:14.328252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:17.120146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:46.940272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:49.497965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:51.609274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:53.804156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:56.176190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:58.602771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:01.162465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:03.734169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:05.879010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:08.035857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:10.246253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:12.371075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:14.499399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:17.276780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:47.106915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:49.634582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:51.752898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:53.965294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:56.346336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:58.756903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:01.305588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:03.873790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:06.032640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:08.195496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:10.387374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:12.512195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:14.650028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:17.449429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:47.312592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:49.793718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:51.914036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:54.131938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:56.529993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:58.943564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:01.465725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:04.040432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:06.184772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:08.372146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:10.546511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:12.666829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:14.816671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:17.610566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:47.480736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:49.951354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:52.064664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:54.297580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:56.693634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:59.129223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:01.617856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:04.208576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:06.335901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:08.525278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:10.692636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:12.811952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:14.969803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:17.757078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:47.649881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:50.093475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:52.216295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:54.446708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:56.864280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:59.310878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:01.773489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:04.351700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:06.472517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:08.672404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:10.839262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:12.950571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:15.130440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:17.917331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:47.810518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:50.241101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:52.366925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:54.611348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:57.045435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:55:59.541575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:01.937630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:04.502329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:06.621645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:08.825537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:10.993394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:13.101201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T16:56:15.310094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-26T16:56:27.175267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
BirthyearIncomeRecencyMntMeat&FishMntEntriesMntVegan&VegetarianMntDrinksMntDessertsMntAdditionalRequestsNumOfferPurchasesNumAppPurchasesNumTakeAwayPurchasesNumStorePurchasesNumAppVisitsMonthEducationMarital_StatusKid_Younger6Children_6to18Response_Cmp1Response_Cmp2Response_Cmp3Response_Cmp4Response_Cmp5Complain
Birthyear1.000-0.2140.004-0.226-0.010-0.126-0.015-0.029-0.064-0.057-0.138-0.155-0.1820.1310.0970.0920.2260.3470.0190.0660.0960.0730.0280.000
Income-0.2141.0000.0230.7960.5570.8040.5550.5590.481-0.2390.5300.7650.689-0.6550.1270.0260.4150.3190.0530.2270.5500.3560.1370.000
Recency0.0040.0231.0000.0280.0110.0270.0060.0230.0110.0060.0170.0330.021-0.0150.0000.0010.0000.0000.0000.0000.0180.0000.0280.014
MntMeat&Fish-0.2260.7960.0281.0000.5170.7970.5150.5190.5760.0110.7360.7900.792-0.3640.0900.0250.4040.0990.0350.3520.4740.3480.2340.000
MntEntries-0.0100.5570.0110.5171.0000.6930.7020.7050.552-0.1390.4500.6180.580-0.4170.0540.0000.3070.1450.0170.0440.2570.2090.0000.017
MntVegan&Vegetarian-0.1260.8040.0270.7970.6931.0000.6910.6940.604-0.0700.6260.8460.731-0.5010.0430.0330.3210.2330.0000.1090.3900.3010.0530.018
MntDrinks-0.0150.5550.0060.5150.7020.6911.0000.6930.547-0.1360.4590.6150.586-0.4180.0450.0000.3000.1460.0300.0560.2500.2010.0230.000
MntDesserts-0.0290.5590.0230.5190.7050.6940.6931.0000.551-0.1340.4640.6150.592-0.4240.0430.0000.2970.1450.0290.0620.2800.2180.0220.000
MntAdditionalRequests-0.0640.4810.0110.5760.5520.6040.5470.5511.0000.0750.5860.6120.549-0.2100.0500.0200.2560.0260.1720.0650.1680.1450.0460.000
NumOfferPurchases-0.057-0.2390.0060.011-0.139-0.070-0.136-0.1340.0751.0000.252-0.0560.0260.4120.0140.0470.2210.3340.0610.0500.2600.1760.0620.000
NumAppPurchases-0.1380.5300.0170.7360.4500.6260.4590.4640.5860.2521.0000.5900.674-0.0460.0560.0300.2860.1210.0440.1890.1970.1790.1030.013
NumTakeAwayPurchases-0.1550.7650.0330.7900.6180.8460.6150.6150.612-0.0560.5901.0000.672-0.5490.0770.0480.4080.1740.0490.2000.3750.3200.1070.000
NumStorePurchases-0.1820.6890.0210.7920.5800.7310.5860.5920.5490.0260.6740.6721.000-0.4170.0700.0250.4010.0850.1420.1910.2410.2010.0950.000
NumAppVisitsMonth0.131-0.655-0.015-0.364-0.417-0.501-0.418-0.424-0.2100.412-0.046-0.549-0.4171.0000.0520.0160.3460.2200.1050.0480.3070.2020.0000.000
Education0.0970.1270.0000.0900.0540.0430.0450.0430.0500.0140.0560.0770.0700.0521.0000.0000.0790.0980.0000.0490.0530.0490.0000.043
Marital_Status0.0920.0260.0010.0250.0000.0330.0000.0000.0200.0470.0300.0480.0250.0160.0001.0000.0460.0570.0210.0470.0320.0460.0180.000
Kid_Younger60.2260.4150.0000.4040.3070.3210.3000.2970.2560.2210.2860.4080.4010.3460.0790.0461.0000.0470.0680.1670.2260.1900.0800.025
Children_6to180.3470.3190.0000.0990.1450.2330.1460.1450.0260.3340.1210.1740.0850.2200.0980.0570.0471.0000.0410.0210.2030.1360.0000.000
Response_Cmp10.0190.0530.0000.0350.0170.0000.0300.0290.1720.0610.0440.0490.1420.1050.0000.0210.0680.0411.0000.0810.0400.0740.0410.000
Response_Cmp20.0660.2270.0000.3520.0440.1090.0560.0620.0650.0500.1890.2000.1910.0480.0490.0470.1670.0210.0811.0000.2740.2380.2360.000
Response_Cmp30.0960.5500.0180.4740.2570.3900.2500.2800.1680.2600.1970.3750.2410.3070.0530.0320.2260.2030.0400.2741.0000.3890.2140.000
Response_Cmp40.0730.3560.0000.3480.2090.3010.2010.2180.1450.1760.1790.3200.2010.2020.0490.0460.1900.1360.0740.2380.3891.0000.2080.000
Response_Cmp50.0280.1370.0280.2340.0000.0530.0230.0220.0460.0620.1030.1070.0950.0000.0000.0180.0800.0000.0410.2360.2140.2081.0000.000
Complain0.0000.0000.0140.0000.0170.0180.0000.0000.0000.0000.0130.0000.0000.0000.0430.0000.0250.0000.0000.0000.0000.0000.0001.000

Missing values

2023-03-26T16:56:18.223091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-26T16:56:18.748041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-26T16:56:19.075323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NameBirthyearEducationMarital_StatusIncomeKid_Younger6Children_6to18Response_Cmp1Response_Cmp2Response_Cmp3Response_Cmp4Response_Cmp5Date_AdherenceRecencyMntMeat&FishMntEntriesMntVegan&VegetarianMntDrinksMntDessertsMntAdditionalRequestsNumOfferPurchasesNumAppPurchasesNumTakeAwayPurchasesNumStorePurchasesNumAppVisitsMonthComplain
CustomerID
5078Mr. Isaac Arnold1969GraduationTogether99861.000010002021-11-03 00:00:0066.05180.014009240.02800.0840.028.01861040
5081Mr. Austin Abraham1964PhDDivorced124326.000000002022-04-17 00:00:006.08450.03008760.01220.0600.030.0153810
5084Mrs. Bernadette Allan1979MasterDivorced31179.010100002022-05-27 00:00:0069.0140.0160150.00.0200.027.0343270
5087Mrs. Kylie Russell1969PhDDivorced102929.001000002021-07-22 00:00:0010.05320.017602520.01060.0880.0133.03941350
5090Mr. Julian Arnold1975MasterMarried101038.001000102021-02-03 00:00:0026.06710.011204770.0900.0900.0216.011181160
5093Mr. Nicholas Smith1977PhDTogether24625.011100002021-10-31 00:00:0065.0260.020390.020.00.016.0552390
5096Mr. Paul Bell1959GraduationMarried76434.011000002020-12-07 00:00:0073.02440.0480660.060.0540.010.0773670
5099Mr. Julian Mathis1960MasterSingle75463.001000002022-03-13 00:00:0044.0340.0060.00.00.01.0131330
5102Mrs. Rose Clarkson1959MasterMarried120449.000001002021-10-31 00:00:0075.05320.025207350.02520.02520.0126.01761110
5105Mr. Michael Duncan1996HighSchoolMarried26231.010000002020-12-18 00:00:0053.030.020045.020.00.01.0121370
NameBirthyearEducationMarital_StatusIncomeKid_Younger6Children_6to18Response_Cmp1Response_Cmp2Response_Cmp3Response_Cmp4Response_Cmp5Date_AdherenceRecencyMntMeat&FishMntEntriesMntVegan&VegetarianMntDrinksMntDessertsMntAdditionalRequestsNumOfferPurchasesNumAppPurchasesNumTakeAwayPurchasesNumStorePurchasesNumAppVisitsMonthComplain
CustomerID
35024Mr. Brian Ball1979GraduationTogether64175.001100002020-09-06 00:00:0052.02840.03401365.0420.0420.0249.0296370
35027Miss Jane North1972GraduationTogether81783.001010002022-04-24 00:00:0097.01360.020180.00.060.032.0262360
35030Mr. Alan Brown1963PhDMarried111021.000000002021-09-14 00:00:006.08520.06803450.0220.0460.069.0195630
35039Mr. Simon Rampling1978HighSchoolDivorced96366.000000002021-10-14 00:00:0018.02790.034401110.0576.0160.0246.014111010
35045Mr. Tim Nolan1959MasterDivorced115490.001000002021-03-01 00:00:0075.010280.016005775.00.02240.0144.0346760
35057Mr. Jacob Grant1960HighSchoolTogether103804.001000002020-09-24 00:00:001.06090.08802820.0520.0700.034.0243970
35063Mr. Thomas Parr1981GraduationTogether59722.000000102021-05-09 00:00:0072.04140.022205445.0200.02220.043.0294560
35066Mr. Trevor Dickens1965GraduationMarried138559.001011002022-04-03 00:00:0075.010170.06606255.01660.02000.016.0176550
35069Mr. Benjamin Rees1979PhDTogether109591.000000002022-05-24 00:00:0098.06750.006390.03220.0410.058.01761230
35072Mrs. Leah Wright1983HighSchoolDivorced122120.010000002022-05-12 00:00:004.06480.04325460.02160.0260.0108.01125640